Please download the {aniMotum} package from R-Universe.
# install from R-universe repository
install.packages("aniMotum",
repos = c("https://cloud.r-project.org",
"https://ianjonsen.r-universe.dev"),
dependencies = TRUE)
This installs all Imported and Suggested R packages from CRAN and R-universe. If queried, answer Yes to install the source version. Note, if you haven’t installed {aniMotum} previously then installation of dependent packages may take a while, especially if many of them need to be compiled. You should only need to go through this once, subsequent installation of {aniMotum} updates will be much faster.
Alternatively you can download a binary version of aniMotum here: https://ianjonsen.r-universe.dev/aniMotum
For full instructions see the aniMotum homepage on GitHub: https://github.com/ianjonsen/aniMotum
The aim of this practical is to give you an understanding of the common approaches that are used to process and analyse animal movement data. We will be doing this in the R statistical framework and will introduce you to some commonly used R packages.
During this practical you will:
This practical will be based on the {tidyverse} style of R coding. Tidyverse is a collection of R packages designed for data science. The key for this practical is that by learning to structure our data in a ‘tidy’ way we can use the same tools in similar ways for different data sets. This helps to make our data processing and analysis reproducible.
If reproducible and open science is something you are interested in have a look at this paper from Lowndes et al. 2017 and this blog post on Tidy Data for reproducibility, efficiency and collaboration.